Incubated state evaluating device, incubated state evaluating method, incubator, and program

ABSTRACT

An incubated state evaluating device includes an image reading unit, a feature value calculating unit, a frequency distribution calculating unit, and an evaluation information generating unit. The image reading unit reads a plurality of images in which a plurality of cells incubated in an incubation container are image-captured in time series. The feature value calculating unit obtains each of feature values representing morphological features of cells from. the images for each of the cells included in the images. The frequency distribution calculating unit obtains each of frequency distributions of the feature values corresponding to the respective images. The evaluation information generating unit generates evaluation information evaluating an incubated state of cells in the incubation container based on a variation of the frequency distributions between images.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of U.S. patentapplication Ser. No. 13/203,310, filed Dec. 2, 2011, which in turn is aU.S. National Stage application claiming the benefit of prior filedInternational Application No. PCT/JP2010/001277, filed Feb. 25, 2010, inwhich the international Application claims a priority date of Feb. 26,2009 based on prior filed Japanese Application No. 2009-044375, theentire contents of which are incorporated herein by reference.

TECHNICAL FIELD

The present application relates to an incubated state evaluating device,an incubated state evaluating method, an incubator, and a programperforming evaluation of an incubated state of cells.

BACKGROUND ART

An art evaluating an incubated state of cells is a basic technology in awide range of fields including a sophisticated medical field such as aregenerative medicine and a screening of medical products. For example,there is a process proliferating and differentiating cells at in vitroin the regenerative medicine field. In the above-stated process, it isinevitable to accurately evaluate the incubated state of cells teamanage results of the differentiation of cells, and presence/absence ofcanceration and infection of cells. As an example, an evaluation methodof cancer cells using a transcription factor as a marker is disclosed inPatent Document 1.

Patent Document 1: Japanese Unexamined Patent Application PublicationNo. 2007-195533 DISCLOSURE Problems to be Solved

However, a pre-process to implement the marker to each cell being anevaluation object in advance is necessary in the above-statedconventional art, and therefore, it is very complicated.

Accordingly, it is still requested to evaluate the incubated state ofcells from an image with high accuracy by a comparatively easy method.

A proposition of the present application is to provide a method toevaluate an incubated state of cells from an image with high accuracy bya comparatively easy method.

Means for Solving the Problems

An incubated state evaluating device according to an aspect includes animage reading unit, a feature value calculating unit, a frequencydistribution calculating unit, and an evaluation information generatingunit. The image reading unit reads a plurality of images in which aplurality of cells incubated in an incubation container areimage-captured in time series. The feature value calculating unitobtains each of feature values representing morphological features ofthe cells from the images for each of the cells included in the images.The frequency distribution calculating unit obtains each of frequencydistributions of the feature values corresponding to the respectiveimages. The evaluation information generating unit generates evaluationinformation evaluating an incubated state of the cells in the incubationcontainer based on a variation of the frequency distributions betweenthe images.

An incubated state evaluating device according to another aspectincludes an image reading unit, a feature value calculating unit, and anevaluation information generating unit. The image reading unit reads aplurality of images in which a plurality of cells incubated in anincubation container are image-captured in time series. The featurevalue calculating unit obtains each of feature values representingmorphological features of the cells from the images for each of thecells included in the images. The evaluation information generating unitgenerates evaluation information predicting a future incubated state ofthe cells in the incubation container by using a variation of thefeature values between the plurality of images.

Note that an incubator incorporating the incubated state evaluatingdevice, a program configured to cause a computer to function as theincubated state evaluating device, a program storage medium, and the onerepresenting operations of the incubated state evaluating device by acategory of method are also effective as concrete aspects of the presentapplication.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an outline of an incubator in oneembodiment.

FIG. 2 is a front view of the incubator in the one embodiment,

FIG. 3 is a plan view of the incubator in the one embodiment.

FIG. 4 is a flowchart illustrating an example of an observationoperation at the incubator.

FIG. 5 is a flowchart illustrating an example of a generation process ofa computation model,

FIG. 6 is a view illustrating an outline of each feature value.

FIGS. 7(a) to 7(c) are histograms each illustrating an example ofvariation over a time lapse of a “Shape Factor”, and FIGS. 7(d) to 7(f)are histograms each illustrating an example of variation over a timelapse of a “Fiber Length”.

FIGS. 8(a) to 8(c) are views each illustrating features of cellmorphology obtained from microscopic image performing a time lapseobservation of a normal cell group.

FIGS. 9(a) to 9(c) are views each illustrating features of cellmorphology obtained from microscopic image performing a time lapseobservation of a cell group in which cancer cells are mixed in normalcells.

FIG. 10 is a view illustrating an outline of an ANN.

FIG. 11 is a view illustrating an outline of an FNN.

FIG. 12 is a view illustrating a sigmoid function in the FNN.

FIG. 13 is a flowchart illustrating an operation example of an incubatedstate evaluating process.

FIGS. 14(a) and 14(b) are views illustrating incubated state examples ofmyoblasts.

FIG. 15 is a histogram illustrating a variation over a time lapse of a“Shape Factor” at an incubation time of the myoblasts.

FIG. 16 is a histogram illustrating a variation over a time lapse of the“Shape Factor” at the incubation time of the myoblasts.

FIG. 17 is a graphic chart illustrating prediction results of eachsample in a first prediction model of an example.

FIG. 18 is a graphic chart illustrating prediction results of eachsample in a second prediction model of an example.

DETAILED DESCRIPTION OF THE EMBODIMENT Configuration Example of OneEmbodiment

Hereinafter, a configuration example of an incubator including anincubated state evaluating device according to one embodiment isdescribed with reference to the drawings. FIG. 1 is a block diagramillustrating an outline of the incubator according to the oneembodiment. Besides, FIG. 2 and FIG. 3 are a front view and a plan viewof the incubator in the one embodiment.

An incubator 11 according to the one embodiment includes an upper casing12 and a lower casing 13. The upper casing 12 is placed on the lowercasing 13 under an assembled state of the incubator 11. Note that aninner space between the upper casing 12 and the lower casing 13 isdivided into upper and lower parts by a base plate 14.

At first, an outline of a configuration of the upper casing 12 isdescribed. A temperature-controlled room 15 incubating cells is formedinside the upper casing 12. The temperature-controlled room 15 includesa temperature regulator 15 a and a humidity regulator 15 b, and aninside of the temperature-controlled room 15 is maintained to be anenvironment suitable for the incubation of cells (for example, anatmosphere at a temperature of 37° C. and with a humidity of 90%) (Notethat the temperature regulator 15 a and the humidity regulator 15 b arenot illustrated in FIG. 2 and FIG. 3).

A large door 16, a middle door 17, and a small door 18 are provided at afront surface of the temperature-controlled room 15. The large door 16covers front surfaces of the upper casing 12 and the lower casing 13.The middle door 17 covers the front surface of the upper casing 12, andisolates environments between the temperature-controlled room 15 andoutside when the large door 16 is opened. The small door 18 is a door tocarry in/out an incubation container 19 incubating cells, and attachedto the middle door 17. It becomes possible to suppress an environmentalchange of the temperature-controlled room 15 by performing the carryingin/out of the incubation container 19 from the small door 18. Note thatairtightnesses of the large door 16, the middle door 17, and the smalldoor 18 are respectively maintained by packings P1, P2 and P3.

Besides, a stocker 21, an observation unit 22, a container conveyor 23,and a conveyor table 24 are disposed at the temperature-controlled room15, Here, the conveyor table 24 is disposed at a near side of the smalldoor 18, to carry it/out the incubation container 19 from the small door18.

The stocker 21 is disposed at a left side of the temperature-controlledroom 15 when it is seen from the front surface of the upper casing 12 (alower side in FIG. 3). The stocker 21 includes plural shelves, andplural incubation containers 19 are able to be housed in respectiveshelves of the stocker 21. Note that cells being the incubation objectsare housed in each of the incubation containers 19 together with aculture medium.

The observation unit 22 is disposed at a right side of thetemperature-controlled room 15 when it is seen from the front surface ofthe upper casing 12. This observation unit 22 is able to execute a timelapse observation of cells inside the incubation container 19.

Here, the observation unit 22 is disposed by being fitted into anopening of the base plate 14 of the upper casing 12. The observationunit 22 includes a sample stage 31, a stand arm 32 projected towardupward of the sample stage 31, and a main body part 33 housing amicroscopic optical system for phase difference observation and animaging device (34). The sample stage 31 and the stand arm 32 aredisposed at the temperature-controlled room 15, on the other hand, themain body part 33 is housed inside the lower casing 13.

The sample stage 31 is made up of a light transmissive material, and theincubation container 19 is able to be placed thereon. The sample stage31 is made up to be able to move in a horizontal direction, and aposition of the incubation container 19 placed at an upper surface canbe adjusted. Besides, an LED light source 38 is housed in the stand arm32. The imaging device 34 is able to acquire features of cell morphologyobtained from microscopic image of cells by capturing images of thecells in the incubation container 19 transilluminated from an upper sideof the sample stage 31 by the stand arm 32, via the microscopic opticalsystem.

The container conveyor 23 is disposed at a center of thetemperature-controlled room 15 when it is seen from the front surface ofthe upper casing 12. The container conveyor 23 performs a transfer ofthe incubation container 19 among the stocker 21, the sample stage 31 ofthe observation unit 22, and the conveyor table 24.

As illustrated in FIG. 3, the container conveyor 23 includes a verticalrobot 34 having an articulated arm, a rotation stage 35, a mini stage36, and an arm part 37. The rotation stage 35 is attached to a tipportion of the vertical robot 34 via a rotation shaft 35 a to be able torotate for 180 degrees in a horizontal direction. It is thereforepossible for the rotation stage 35 to face the arm part 37 relative toeach of the stocker 21, the sample stage 31, and the conveyor table 24.

Besides, the mini stage 36 is attached to be able to slide in thehorizontal direction relative to the rotation stage 35. The arm part 37gripping the incubation container 19 is attached to the mini stage 36.

Next, an outline of a configuration of the lower casing 13 is described.The main body part 33 of the observation unit 22 and a control device 41of the incubator 11 are housed inside the lower casing 13.

The control device 41 is coupled to each of the temperature regulator 15a, the humidity regulator 15 b, the observation unit 22, and thecontainer conveyor 23. The control device 41 totally controls each partof the incubator 11 in accordance with a predetermined program.

As an example, the control device 41 maintains inside thetemperature-controlled room 15 to be a predetermined environmentalcondition by controlling each of the temperature regulator 15 a and thehumidity regulator 15 b. Besides, the control device 41 controls theobservation unit 22 and the container conveyor 23 based on apredetermined observation schedule, and automatically executes anobservation sequence of the incubation container 19. Further, thecontrol device 41 executes an incubated state evaluating processperforming evaluation of the incubated state of cells based on theimages acquired by the observation sequence.

Here, the control device 41 includes a CPU 42 and a storage unit 43. TheCPU 42 is a processor executing various calculation processes of thecontrol device 41. Besides, the CPU 42 functions as each of a featurevalue calculating unit 44, a frequency distribution calculating unit 45,and an evaluation information generating unit 46 by the execution of theprogram (note that operations of the feature value calculating unit 44,the frequency distribution calculating unit 45, and the evaluationinformation generating unit 46 are described later).

The storage unit 43 is made up of nonvolatile storage media such as ahard disk, a flash memory, and so on. Management data relating to eachincubation container 19 housed at the stocker 21 and data of thefeatures of cell morphology obtained from microscopic image captured bythe imaging device are stored at the storage unit 43. Further, theprograms executed by the CPU 42 are stored at the storage unit 43.

Note that (a) index data representing individual incubation containers19, (b) housed positions of the incubation containers 19 at the stocker21, (c) kinds and shapes (well plate, dish, flask, and so on) of theincubation containers 19, (d) kinds of cells incubated at the incubationcontainer 19, (e) the observation schedule of the incubation container19, (f) imaging conditions at the time lapse observation time (amagnification of an objective lens, observation points inside thecontainer, and so on), or the like are included in the above-statedmanagement data. Besides, the management data are generated by eachsmall container as for the incubation container 19 capable ofsimultaneously incubating cells in plural small containers such as thewell plate.

Example of Observation Operation in One Embodiment

Next, an example of observation operations at the incubator 11 in theone embodiment are described with reference to a flowchart in FIG. 4.FIG. 4 illustrates an operation example in which the time lapseobservation of the incubation container 19 carried into thetemperature-controlled room 15 is performed in accordance with aregistered observation schedule.

Step S101: The CPU 42 judges whether or not an observation start time ofthe incubation container 19 arrives by comparing the observationschedule of the management data of the storage unit 43 and a currentdate and time. When it is the observation start time (YES side), the CPU42 transfers the process to S102. On the other hand, when it is not theobservation time of the incubation container 19 (NO side), the CPU 42waits until the next observation schedule time.

Step S102: The CPU 42 instructs the container conveyor 23 to convey theincubation container 19 corresponding to the observation schedule. Thecontainer conveyor 23 carries out the indicated incubation container 19from the stocker 21 and places on the sample stage 31 of the observationunit 22. Note that an entire observation image of the incubationcontainer 19 is captured by a bird view camera (not-illustrated) housedin the stand arm 32 at a phase When the incubation container 19 isplaced on the sample stage 31.

Step S103: The CPU 42 instructs the observation unit 22 to capture thefeatures of cell morphology obtained from microscopic image of thecells. The observation unit 22 illuminates the incubation container 19by lighting the LED light source 38, and captures the features of cellmorphology obtained from microscopic image of the cells inside theincubation container 19 by driving the imaging device 34.

At this time, the imaging device 34 captures the features of cellmorphology obtained from microscopic image based on the imagingconditions (the magnification of the objective lens, the observationpoints inside the container) specified by a user based on the managementdata stored at the storage unit 43. For example, when plural pointsinside the incubation container 19 are observed, the observation unit 22sequentially adjusts the position of the incubation container 19 by thedrive of the sample stage 31, to capture each features of cellmorphology obtained from microscopic image at each point. Note that thedata of the features of cell morphology obtained from microscopic imageacquired at the S103 is read into the control device 41, and stored tothe storage unit 43 by the control of the CPU 42.

Step S104: The CPU 42 instructs the container conveyor 23 to convey theincubation container 19 after a completion of the observation schedule.The container conveyor 23 conveys the indicated incubation container 19from the sample stage 31 of the observation unit 22 to a predeterminedhousing position of the stocker 21. After that, the CPU 42 finishes theobservation sequence to return the process to the S101. The descriptionof the flowchart in FIG. 4 is finished.

Incubated State Evaluating Process in One Embodiment

Next, an example of the incubated state evaluating process in the oneembodiment is described. In the one embodiment, an example in which thecontrol device 41 estimates a mixed ratio of cancer cells in incubatedcells of the incubation container 19 by using plural features of cellmorphologies obtained from microscopic images acquired by performing thetime lapse observation of the incubation container 19 is described.

The control device 41 in the incubated state evaluating process findsfrequency distributions of feature values representing morphologicalfeatures of cells from the above-stated features of cell morphologiesobtained from microscopic images. The control device 41 generatesevaluation information in which the mixed ratio of cancer cells isestimated based on a variation over a time lapse of the frequencydistribution. Note that the control device 41 generates the evaluationinformation by applying the acquired evaluation information to acomputation model generated in advance by a supervised learning.

Example of Generation Process of Computation Model

Hereinafter, an example of a generation process of the computation modelbeing a pre-process of the incubated state evaluating process isdescribed with reference to a flowchart in FIG. 5. In the generationprocess of the computation model, the control device 41 determines acombination of the frequency distributions used for the generation ofthe evaluation information from plural combinations of the frequencydistributions of which photographing time of the image and kinds of thefeature values are each different.

In the example in FIG. 5, features of cell morphology obtained frommicroscopic image group of a sample is prepared in advance by performingthe time lapse observation of the incubation container 19 where a cellgroup in which cancer cells are mixed is incubated by the incubator 11at the same view filed with the same photographing condition. Note thatin the features of cell morphology obtained from microscopic image ofthe sample, a total number of the cells and the number of cancer cellsincluded in each image are each known not from the image but by beingexperimentally counted.

In the example in FIG. 5, the time lapse observation of the incubatedcontainer 19 is performed until 72 hours elapsed by every eight hourswhile a time when eight hours elapsed from the incubation start is setas a first time. Accordingly, in the example in FIG. 5, nine pieces (8h, 16 h, 24 h, 32 h, 40 h, 48 h, 56 h, 64 h, 72 h) of the features ofcell morphologies obtained from microscopic images of the sample ofwhich incubation container 19 and observation point are in common areacquired as one set. Note that in the example in FIG. 5, the features ofcell morphologies obtained from microscopic images of the sample areprepared for plural sets by performing the time lapse observation of theplural incubation containers 19 respectively. Besides, plural featuresof cell morphologies obtained from microscopic images photographingplural points (for example, five points observation or the whole of theincubation container 19) of the same incubation container 19 at the sameobservation time zone may be treated as an image for one time of thetime lapse observation.

Step S201: The CPU 42 reads the data of the features of cellmorphologies obtained from microscopic images of the sample prepared inadvance from the storage unit 43. Note that the CPU 42 in the S201acquires information representing the total number of cells and thenumber of cancer cells corresponding to each image at this time.

Step S202: The CPU 42 specifies the image to be a process object fromamong the features of cell morphologies obtained from microscopic imagesof the sample (S201). Here, the CPU 42 at the S202 sequentiallyspecifies all of the features of cell morphologies obtained frommicroscopic images of samples prepared in advance as the processobjects.

Step S203: The feature value calculating unit 44 extracts the cellsincluded in the image as for the features of cell morphologies obtainedfrom microscopic images being the process objects (S202). For example,when the cells are captured by a phase contrast microscope, a haloappears at a periphery of a portion of which change of the phasedifference is large such as a cell wall. Accordingly, the feature valuecalculating unit 44 extracts the halo corresponding to the cell wall bya publicly known edge extracting method, and estimates that a closedspace surrounded by an edge by a contour tracing process is a cell. Itis thereby possible to extract individual cells from the features ofcell morphology obtained from microscopic image.

Step S204: The feature value calculating unit 44 finds each of featurevalues representing morphological features of the cell as for each cellextracted from the image at the S203. The feature value calculating unit44 at the S204 finds the following 16 kinds of feature valuesrespectively as for each cell.

Total Area (Refer to FIG. 6(a))

A “total area” is a value representing an area of a focused cell. Forexample, the feature value calculating unit 44 is able to find the valueof the “total area” based on the number of pixels of a region of thefocused cell.

Hole Area (Refer to FIG. 6(b))

A “hole area” is a value representing an area of a “hole” in the focusedcell. Here, the “hole” means a part in which intensity of image in thecell is a threshold value or more by a contrast (a place to be a nearwhite state in the phase difference observation). For example, alysosome of a cell organelle (the lysosome was confirmed by staininglater but not in the actual image) and so on are detected as the “hole”.Besides, a cell nucleus and the other cell organelle may be detected asthe “hole” depending on the image. Note that the feature valuecalculating unit 44 detects a group of pixels of which luminance valuein the cell is the threshold value or more as the “hole”, and may findthe value of the “hole area” based on the number of pixels of the“hole”.

Relative Hole Area (refer to FIG. 6(c))

A “relative hole area” is a value in which the value of the “hole area”is divided by the value of the “total area” (relative hole area =holearea/total area). The “relative hole area” is a parameter representing apercentage of the cell organelle in a size of the cell, and the valuevaries in accordance with, for example, a hypertrophy of the cellorganelle, deterioration of a shape of a nucleus, and so on.

Perimeter (Refer to FIG. 6(d))

A “perimeter” is a value representing a length of an outer periphery ofthe focused cell. For example, the feature value calculating unit 44 isable to acquire the value of the “perimeter” by the contour tracingprocess when the cell is extracted.

Width (Refer to FIG. 6(e))

A “width” is a value representing a length in an image lateral direction(X direction) of the focused cell.

Height (Refer to FIG. 6(f))

A “height” is a value representing a length in an image verticaldirection (Y direction) of the focused cell.

Length (Refer to FIG. 6(g))

A “length” is a value representing a maximum value among lines gettingacross the focused cell (an entire length of the cell).

Breadth (Refer to FIG. 6(h))

A “breadth” is a value representing a maximum value among linesorthogonal to the “length” (a width of the cell).

Fiber Length (Refer to FIG. 6(i))

A “fiber length” is a value representing a length when the focused cellis artificially assumed to be liner. The feature value calculating unit44 finds the value of the “fiber length” by the following expression(1).

[Expression 1]

Fiber Length=¼(P+√{square root over (P ²−16A)})   (1)

Note that in the expression in the present specification, a character“P” represents the value of the “perimeter”. Similarly, a character “A”represents the value of the “total area”.

Fiber Breadth (refer to FIG. 6(j))

A “fiber breadth” is a value representing a width (a length in adirection orthogonal to the “fiber length”) when the focused cell isartificially assumed to be liner. The feature value calculating unit 44finds the value of the “fiber breadth” by the following expression (2).

[Expression 2]

Fiber Breadth=¼(P−√{square root over (P ²−16A)})   (2)

Shape Factor (Refer to FIG. 6(k))

A “Shape factor” is a value representing a circular degree (roundness ofthe cell) of the focused cell. The feature value calculating unit 44finds the value of the “shape factor” by the following expression (3).

$\begin{matrix}\left\lbrack {{Expression}\mspace{14mu} 3} \right\rbrack & \; \\{{{Shape}\mspace{14mu} {Factor}} = \frac{4\pi \; A}{P^{2}}} & (3)\end{matrix}$

Elliptical form Factor (Refer to FIG. 6(l))

An “elliptical form factor” is a value in which the value of the“length” is divided by the value of the “breadth” (Elliptical formfactor=length/breadth), and is a parameter representing a degree ofslenderness of the focused cell.

Inner Radius (Refer to FIG: 6(m))

An “inner radius” is a value representing a radius of an incircle of thefocused cell.

Outer Radius (Refer to FIG. 6(n))

An “outer radius” is a value representing a radius of a circumcircle ofthe focused cell.

Mean Radius (Refer to FIG. 6(o))

A “mean radius” is a value representing an average distance between allpoints making up a contour of the focused cell and a gravity centerpoint thereof.

Equivalent Radius (Refer to FIG. 6(p))

An “equivalent radius” is a value representing a radius of a circlehaving the same area with the focused cell. A parameter of the“equivalent radius” represents a size when the focused cell is virtuallyapproximated to a circle.

Here, the feature value calculating unit 44 may find the above-statedeach feature value by adding error amounts to the number of pixelscorresponding to the cell. At this time, the feature value calculatingunit 44 may find the feature values in consideration of thephotographing conditions (a photographing magnification, an aberrationof the microscopic optical system, and so on) of the features of cellmorphology obtained from microscopic image. Note that the feature valuecalculating unit 44 may find the gravity center point of each cell basedon a publicly known gravity calculation method, and may find eachparameter based on the gravity center point when the “inner radius”, the“outer radius”, the “mean radius”, the “equivalent radius” are found.

Step S205: The feature value calculating unit 44 records each of the 16kinds of feature values of each cell (S204) to the storage unit 43 asfor the features of cell morphologies obtained from microscopic imagesbeing the process objects (S202).

Step S206: The CPU 42 judges whether or not all of the features of cellmorphologies obtained from microscopic images are already processed (astate in which feature values of each cell are already acquired in thefeatures of cell morphologies obtained from microscopic images of all ofthe samples). When the above-stated requirement is satisfied (YES side),the CPU 42 transfers the process to S207. On the other hand, when theabove-stated requirement is not satisfied (NO side), the CPU 42 returnsto the S202, and repeats the above-stated operations while setting theother features of cell morphologies obtained from microscopic imageswhich are not processed as the process objects.

Step S207: The frequency distribution calculating unit 45 finds afrequency distribution. of the feature value by each kind of featurevalue as for each features of cell morphology obtained from microscopicimage. Accordingly, the frequency distribution calculating unit 45 atthe S207 finds the frequency distributions of 16 kinds of feature valuesfor the features of cell morphologies obtained from microscopic imagesacquired by one time observation. Besides, the number of cellscorresponding to each division of the feature values is found as afrequency (%) in each frequency distribution.

Besides, the frequency distribution calculating unit 45 at the S207normalizes the division of the frequency in the above-stated frequencydistribution by using a standard deviation by each kind of the featurevalue. Here, a case when the division at the frequency distribution ofthe “shape factor” is determined is described as an example.

At first, the frequency distribution calculating unit 45 calculates thestandard deviation of all of the values of the “shape factor” found fromeach of the features of cell morphologies obtained from microscopicimages. Next, the frequency distribution calculating unit 45 substitutesthe value of the standard deviation to an expression of a Fisher, tofind a reference value of the division of the frequency in the frequencydistribution of the “shape factor”. At this time, the frequencydistribution calculating unit 45 divides the standard deviation (S) ofthe all values of the “shape factor” by four, and round off at the thirddecimal place to set it as the reference value. Note that the frequencydistribution calculating unit 45 of the one embodiment plots divisionsfor 20 series on a monitor and so on, when the frequency distribution isillustrated as a histogram.

As an example, when the standard deviation S of the “shape factor” is259, “64.75” becomes the reference value because 259/4=64.750. When thefrequency distribution of the “shape factor” of the focused image isfound, the frequency distribution calculating unit 45 classifies thecells into each class set by every 64.75 from “0” (zero) value inaccordance with the value of the “shape factor”, and the number of cellsin each class is counted.

As stated above, the frequency distribution calculating unit 45normalizes the division of the frequency distribution with the standarddeviation by each kind of the feature value, and therefore, it ispossible to approximate a tendency of the frequency distribution betweenthe different feature values in a large sense. Accordingly, it iscomparatively easy in the one embodiment to find a correlation betweenthe incubated state of cells and the variation of the frequencydistribution between the different feature values.

Here, FIG. 7(a) is a histogram illustrating a variation over a timelapse of the “shape factor” when an initial mixture ratio of the cancercell is “0” (zero) %. FIG. 7(b) is a histogram illustrating a variationover a time lapse of the “shape factor” when the initial mixture ratioof the cancer cell is 6.7%. FIG. 7(c) is a histogram illustrating avariation over a time lapse of the “shape factor” when the initialmixture ratio of the cancer cell is 25%.

Besides, FIG. 7(d) is a histogram illustrating a variation over a timelapse of the “fiber length” when the initial mixture ratio of the cancercell is “0” (zero) %. Note that the histogram is illustrated only up toa value of “fiber length=323” for easy to understanding in the drawing.FIG. 7(e) is a histogram illustrating a variation over a time lapse ofthe “fiber length” when the initial mixture ratio of the cancer cell is63%. FIG. 7(f) is a histogram illustrating a variation over a time lapseof the “fiber length” when the initial mixture ratio of the cancer cellis 25%.

Step S208: The evaluation information generating unit 46 finds thevariation over the time lapse of the frequency distribution by each kindof feature value.

The evaluation information generating unit 46 at the S208 combines twofrequency distributions of Which kinds of feature values are the sameand photographing times are different among the frequency distributions(9×16) acquired from the features of cell morphologies obtained frommicroscopic images for one set. As an example, the evaluationinformation generating unit 46 respectively combines the frequencydistributions at eight hours elapsed and 16 hours elapsed, the frequencydistributions at eight hours elapsed and 24 hours elapsed, the frequencydistributions at eight hours elapsed and 32 hours elapsed, the frequencydistributions at eight hours elapsed and 40 hours elapsed, the frequencydistributions at eight hours elapsed and 48 hours elapsed, the frequencydistributions at eight hours elapsed and 56 hours elapsed, the frequencydistributions at eight hours elapsed and 64 hours elapsed, the frequencydistributions at eight hours elapsed and 72 hours elapsed of which kindsof feature values are the same and photographing times are different asfor nine frequency distributions. Namely, when the feature value of onekind in one set is focused, eight kinds of combinations as a total aregenerated per the frequency distributions of the feature value.

The evaluation information generating unit 46 finds a variation of thefrequency distribution (an absolute value of the difference of thefrequency distributions between images is integrated) by the followingexpression (4) as for each of the eight kinds of combinations.

$\begin{matrix}{\mspace{79mu} \left\lbrack {{Expression}\mspace{14mu} 4} \right\rbrack} & \; \\{{{variation}\mspace{14mu} {of}\mspace{14mu} {frequency}\mspace{14mu} {distribution}} = {\sum\limits_{i}\; {{{Control}_{i} - {Sample}_{i}}}}} & (4)\end{matrix}$

Note that in the expression (4), the “control” represents a frequency(the number of cells) for one division at the frequency distribution inan initial state (at eight hours elapsed). Besides, the “sample”represents the frequency for one division at a frequency distributionbeing a comparison object. Further, “i” is a variable representing adivision of the :frequency distribution,

The evaluation info kation generating unit 46 is able to acquire eightways of variations of the frequency distributions as for each of all ofthe feature values by performing the above stated calculation by eachkind of the feature value. Namely, it is possible to obtain 16 kinds×8ways of 128 combinations of the frequency distributions as for thefeatures of cell morphologies obtained from microscopic images for oneset. Hereinafter, one combination of the frequency distributions isrepresented only as an “index” in the present specification. Note thatit goes without saying that the evaluation information generating unit46 at the S208 finds each of the variations of the 128 frequencydistributions corresponding to respective indexes in the plural sets ofthe features of cell morphologies obtained from microscopic images.

Here, a reason focusing on the variation over the time lapse of thefrequency distribution in the one embodiment is described. FIGS. 8 eachillustrate the features of cell morphology obtained from microscopicimage when a normal cell group the initial mixture ratio of cancer cellis “0” (zero) %) is incubated in the incubator 11, and the time lapseobservation is performed. Note that the histograms illustrating thefrequency distributions of the “shape factor” found from the respectivefeatures of cell morphologies obtained from microscopic images are FIG.8 are illustrated in FIG. 7(a).

In this case, the number of cells increases in accordance with the timelapse in each of the images in FIG. 8, but the frequency distributionscorresponding to each of the images maintain almost the same shape inthe histograms of the “shape factor” illustrated FIG. 7(a).

On the other hand, FIGS. 9 each illustrate the features of cellmorphology obtained from microscopic image when the normal cell group towhich the cancer cells are mixed in advance for 25% is incubated in theincubator 11, and the time lapse observation is performed. Note that thehistograms representing the frequency distributions of the “shapefactor” found from the respective features of cell morphologies obtainedfrom microscopic images in FIG. 9 are illustrated in FIG. 7(c)

In this case, a ratio of the cancer cells (rounded cells) of whichshapes are different from the normal cell increases in accordance withthe time lapse in each of the images in FIG. 9. Accordingly, a largevariation appears in the shapes of the frequency distributionscorresponding to the respective images in accordance with the time lapsein the histograms of the “shape factor” illustrated in FIG. 7(c). Itturns out that the variation over the time lapse of the frequencydistribution is strongly correlated with the mixture of the cancercells. The inventors perform the evaluation of the incubated cell statefrom the information of the variation over the time lapse of thefrequency distribution based on the above-stated knowledge.

Step S209: The evaluation information generating unit 46 specifies oneor more of indexes properly reflecting the incubated state of cells by amultivariate analysis from among the 128 indexes. In a selection ofcombination of the indexes, a usage of a linear model is effective inaccordance with the cells and a complexity of the morphology thereof inaddition to a nonlinear model equivalent to a later-described fuzzyneural network. The evaluation information generating unit 46 finds acomputation model deriving the number of cancer cells from the featuresof cell morphologies obtained from microscopic images by using theabove-stated specified one or more indexes together with the selectionof the combination of indexes as stated above.

Here, the evaluation information generating unit 46 at the S209 findsthe computation model by a Fuzzy Neural Network (FNN) analysis.

The FNN is a method combining an Artificial Neural Network (ANN) and afuzzy inference. In the FNN, a decision of a membership function isperformed automatically by incorporating the ANN into the fuzzyinference so as to avoid a portion depending the decision on a humanbeing which is a defect of the fuzzy inference. The ANN being one oflearning machines (refer to FIG. 10) is the one in which a neuralnetwork in a brain of a living human body is mathematically modeled, andhas characteristics described below. The learning in the ANN is aprocess in which a model is built such that an output value approximatesto a supervisor value by changing a coupling load in a circuit couplingbetween nodes (represented by circles in FIG. 10) so that an errorbetween the supervisor value and the output value (Y) becomes small by aback propagation (BP) method by using a data the earning (input value:X) having an objective output value (supervisor value). According tothis BP method, it is possible for the ANN to automatically acquireknowledge by the learning. It is possible to evaluate a generalversatility of the model by finally inputting data which is not used forthe learning. Conventionally, the decision of the membership function isdependent on a human sense, but it becomes possible to identify themembership function automatically by incorporating the ANN as statedabove into the fuzzy inference. This is the FNN. In the FNN, the BPmethod is used, and thereby it is possible to automatically identify andmodeling an input/output relationship given to a network by changing thecoupling load as same as the ANN. The FNN has a characteristic in whicha knowledge can be acquired as a linguistic rule which is easy to beunderstood by the human being such as the fuzzy inference (refer to adialog balloon at a lower right in FIG. 11 as an example) by analyzingthe model after the learning. Namely, the FNN automatically determinesan optimum combination of the fuzzy inference in the combination ofvariables such as numeric values representing the morphological featuresof the cells from a structure, the features thereof, and it is possibleto simultaneously perform an estimation relating to a prediction targetand a generation of rules representing the combinations of the indexeseffective for the prediction.

The structure of the FNN is made up of four layers of an “input layer”,a “membership function part (antecedent part)” determining parametersWe, Wg included in a sigmoid function, a “fuzzy rule part (consequentpart)” capable of determining Wf and extracting a relationship betweenan input and an output as a rule, and an “output layer” (refer to FIG.11). There are We, Wg, Wf in the coupling loads determining the modelstructure of the FNN. The coupling load We determines a center positionof the sigmoid function used for the membership function, and the Wgdetermines a gradient at the center position (refer to FIG. 12). Withinthe model, an input value is expressed with flexibility near the humansense by a fuzzy function (refer to a dialog balloon at a lower left inFIG. 11 as an example). The coupling load Wf represents a contributionof each fuzzy area for an estimation result, and a fuzzy rule can bederived from the Wf. Namely, the structure inside the model can bedecoded afterward, and it can be written as the rule (refer to a dialogballoon at a lower right in FIG. 11 as an example).

The Wf value being one of the coupling loads is used to create the fuzzyrule in the FNN analysis. When the Wf value is a positive value andlarge, a unit makes a large contribution to be judged as “efficient forthe prediction”, and the index lit to the rule is judged to be“effective”. When the Wf value is a negative value and small, the unitmakes a large contribution to be judged as “not efficient for theprediction”, and the index fit to the rule is judged to be “noteffective”.

As a more concrete example, the evaluation information generating unit46 at the S209 finds the above-stated computation model by the processesof the following (A) to (H).

(A) The evaluation information generating unit 46 selects one index fromalong 128 indexes.

(B) The evaluation information generating unit 46 finds the number ofcancer cells (prediction value) in a set of respective features of cellmorphologies obtained from microscopic images by a calculationexpression in Which the variation of the frequency distribution by theindex selected at the (A) is set as a variable.

A calculation expression to find the number of the cancer cells from oneindex is assumed to be “Y=αX₁” (note that the “Y” is a calculated valueof the cancer cells (for example, a value representing an increasednumber of the cancer cells), the “X₁” is the variation of the frequencydistribution corresponding to the selected index (the one found at theS208), and the “α” is a coefficient value corresponding to the “X₁”,respectively). At this time, the evaluation information generating unit46 substitutes an arbitrary value to the “α”, and substitutes eachvariation of the frequency distribution in each set to the “X₁”. Thecalculated value (Y) of the cancer cells in each set is thereby found.

(C) The evaluation information generating unit 46 finds each errorbetween the calculated value Y found at the (B) and the actual number ofcancer cells (supervisor value) as for each set of the features of cellmorphologies obtained from microscopic images. Note that the supervisorvalue is found by the evaluation information generating unit 46 based onthe information of the number of cancer cells read at the S201.

The evaluation information generating unit 46 corrects the coefficient“α” of the calculation expression by the supervised learning such thatthe error of the calculated value at each set of the features of cellmorphologies obtained from microscopic images becomes smaller.

(D) The evaluation information generating unit 46 repeats the processesof the (B) and the (C), and acquires a model of the calculationexpression in which an average error of the calculated values becomesthe smallest as for the index of the (A).

(E) The evaluation information generating unit 46 repeats respectiveprocesses of the (A) to the (D) as for each of the 128 indexes. Theevaluation information generating unit 46 compares the average errors ofthe calculated values in each of the 128 indexes, and sets the index ofwhich average error becomes the lowest to be a first index used for thegeneration of the evaluation information.

(F) The evaluation information generating unit 46 finds a second indexto be combined with the first index found at the (E). At this time, theevaluation information generating unit 46 pairs the first index with theremaining 127 indexes one by one. Next, the evaluation informationgenerating unit 46 finds a prediction error of the cancer cells by acalculation expression in each pair.

A calculation expression to find the number of the cancer cells from twoindexes is assumed to be “Y=αX₁+βX₂” (note that the “Y” represents thecalculated value of the cancer cells, the “X₁” represents the variationof the frequency distribution corresponding to the first index, the “α”represents a coefficient value corresponding to the “X₁”, the “X₂” is avariation of a frequency distribution corresponding to a selected index,the “β” is a coefficient value corresponding to the “X₂” respectively).The evaluation information generating unit 46 finds the values of thecoefficients “α”, “β” such that the average error of the calculatedvalues is the smallest by the similar processes as the (B) and the (C).

After that the evaluation information generating unit 46 compares theaverage errors of the calculated values found at the respective pairs,and finds the pair of which average value is the lowest. The evaluationinformation generating unit 46 sets the indexes of the pair of whichaverage errors are the lowest to be the first and the second index usedfor the generation of the evaluation information.

(G) The evaluation information generating unit 46 terminates acalculation process at a stage when a predetermined terminationcondition is satisfied. For example, the evaluation informationgenerating unit 46 compares the average errors by the respectivecalculation expressions before and after the index is added. Theevaluation information generating unit 46 terminates the calculationprocess of the S209 when the average error of the calculation expressionafter the index is added is higher than the average error of thecalculation expression before the index is added (or when the differenceof both is within a tolerance range).

(H) On the other hand, when the termination condition is not satisfiedat the (G), the evaluation information generating unit 46 further addsthe number of indexes to repeat the similar processes as the (F) and the(G). Accordingly, a narrow down of the indexes is performed by astepwise variable selection when the computation model is found.

Step S210: The evaluation information generating unit 46 recordsinformation of the computation model found at the S209 (informationrepresenting each index used for the calculation expression, informationof the coefficient values corresponding to each index in the calculationexpression, and so on) to the storage unit 43. Hereinabove, thedescription of FIG. 5 is finished.

Here, a computation model of which prediction accuracy of thecontamination rate of cancer cells is 93.2% can be acquired when threeof the “combination of the frequency distributions of the ‘shape factor’at eight hours elapsed and 72 hours elapsed”, the “combination of thefrequency distributions of the ‘perimeter’ at eight hours elapsed and 24hours elapsed”, and the “combination of the frequency distributions ofthe ‘length’ at eight hours elapsed and 72 hours elapsed” are used asthe indexes of the computation model.

Besides, a computation model of which prediction accuracy of thecontamination rate of cancer cell is 95.5% can be acquired when six ofthe “combination of the frequency distributions of the ‘shape factor’ ateight hours elapsed and 72 hours elapsed”, the “combination of thefrequency distributions of the ‘fiber breadth’ at eight hours elapsedand 56 hours elapsed”, the “combination of the frequency distributionsof the ‘relative hole area’ at eight hours elapsed and 72 hourselapsed”, the “combination of the frequency distributions of the ‘shapefactor’ at eight hours elapsed and 24 hours elapsed”, the “combinationof the frequency distributions of the ‘breadth’ at eight hours elapsedand 72 hours elapsed”, and the “combination of the frequencydistributions of the ‘breadth’ at eight hours elapsed and 64 hourselapsed” are used as the indexes of the computation model.

Example of Incubated State Evaluating Process

Next, an operation example of the incubated state evaluating process isdescribed with reference to a flowchart in FIG. 13.

Step S301: The CPU 42 reads the data of the plural features of cellmorphologies obtained from microscopic images to be the evaluationobjects from the storage unit 43. Here, the features of cellmorphologies obtained from microscopic images being the evaluationobjects are the ones acquired by performing the time lapse observationof the incubation containers 19 incubating the cell groups at the samevisual field with the same photographing conditions by the incubator 11.Besides, the time lapse observation in this case is performed by everyeight hours up to 72 hours elapses while setting the time when eighthours elapsed after the incubation start time as the first time to alignthe conditions with the example in FIG. 5.

Step S302: The CPU 42 reads the information of the computation model ofthe storage unit 43 (the one recorded at the S210 in FIG. 5).

Step S303: The feature value calculating unit 44, the frequencydistribution calculating unit 45, and the evaluation informationgenerating unit 46 each find the variation of the frequency distributionas for each index corresponding to the variables of the above-statedcomputation model. The process in the S303 correspond to the S203, S204,S207, S208 in FIG. 5, and therefore, the redundant description is notgiven.

Step S304: The evaluation information generating unit 46 substitutes thevariation of the frequency distribution of each index found at the S303into the computation model read at the S302 to perform the calculation.The evaluation information generating unit 46 generates the evaluationinformation representing the mixture ratio of the cancer cells in thefeatures of cell morphologies obtained from microscopic images being theevaluation object based on the calculation result. After that, theevaluation information generating unit 46 displays the evaluationinformation on a not-illustrated monitor or the like. Hereinabove, thedescription of FIG. 13 is finished.

According to the one embodiment, it is possible for the control device41 to accurately predict the mixture ratio of the cancer cells from thevariation over the time lapse of the frequency distribution of thefeature value by using the features of cell morphologies obtained frommicroscopic images acquired by the time lapse observation. Besides, itis possible for the control device 41 of the one embodiment to set thecells as it is to be the evaluation object, and therefore, it isextremely effective when, for example, the cells incubated for ascreening of medical products and a regenerative medicine are evaluated.

Note that in the one embodiment, the example evaluating the mixtureratio of the cancer cells from the features of cell morphologiesobtained from microscopic images is described, but for example, it ispossible to use the control device 41 for evaluation of a degree of aninduction of differentiation of an embryonic stem cell (ES cell) and aninduced pluripotent stem cell (iPS cell). Besides, the evaluationinformation found in the one embodiment is able to be used as anabnormality detection means of a differentiation, a dedifferentiation, atumor cancer, an activation deterioration, a contamination of cells, andso on in the incubation cell group being the evaluation object, and as ameans to engineeringly manage a quality of the incubation cell groupbeing the evaluation object.

EXAMPLE

Hereinafter, an example of a differentiation prediction of myoblasts isdescribed as an example of the one embodiment. An application of thisdifferentiation prediction of the myoblasts is expected in, for example,a quality control in a myoblast sheet transplantation performed as oneof treatments for a heart disease, a quality control in a regenerativetherapy of muscular tissues, and so on.

When a component of a culture medium is changed caused by lowering of aserum. concentration at the incubation time of the myoblasts, adifferentiation from the myoblast to a myotube cell occurs, and it ispossible to create intramuscular tissues. FIG. 14(a) illustrates anexample of the incubated state of the myoblasts, and FIG. 14(b)illustrates an example in which the myoblasts are differentiated.

Besides, FIG. 15, FIG. 16 are histograms each illustrating a variationover a time lapse of the “shape factor” at the incubation time of themyoblasts. FIG. 15 illustrates the frequency distributions of the “shapefactor” at “0” (zero) hour elapsed and 112 hours elapsed when thedifferentiation is recognized in the myoblasts (serum is 4%). FIG. 16illustrates the frequency distributions of the “shape factor” at “0”(zero) hour elapsed and 112 hours elapsed when the differentiation isnot recognized in the myoblasts (high serum condition). Note that thefrequency distribution at “0” (zero) hour elapsed is represented by adotted line and the frequency distribution at 112 hours elapsed isrepresented by a solid line in each of FIG. 15 and. FIG. 16.

When the two histograms in FIG. 15 are compared, there is a largevariation in the shapes of the two. On the other hand, when the twohistograms in FIG. 16 are compared, there is not such a large variation.Accordingly, it turns out that there is the variation in the histogramin accordance with a variation in the differentiation of the myoblasts(a mixture ratio of the differentiated myoblasts).

Here, in the example, the time lapse observation is performed as for 72pieces of samples of the myoblasts by the incubator according to the oneembodiment at eight hours interval up to the fifth day respectively. Acontrol device (an incubated state evaluating device) performs asdifferentiation prediction of the myoblasts by the following two stagesof processes.

At the first stage, a two group discrimination model alternativelydiscriminating presence/absence of the differentiation of the myoblastsis generated by the control device based on a generation process of theabove-stated computation model. Specifically, the control device selectsan index from among all of the indexes acquired from the features ofcell morphologies obtained from microscopic images from eight hourselapsed to 32 hours elapsed after the observation is started, anddevelops a first discrimination model finding a degree ofdifferentiation of the myoblasts, and a second discrimination modeldiscriminating presence/absence of the differentiation by a thresholdvalue from the degree of differentiation found by the firstdiscrimination model. The control device separates each of the 72 piecesof samples into two groups in accordance with the presence/absence ofthe differentiation by a discriminant analysis according to the firstdiscrimination model and the second discrimination model. As a result,the control device is able to discriminate the presence/absence of thedifferentiation correctly in all of the 72 pieces of samples.

At a second stage, a prediction model predicting the degree ofdifferentiation of the myoblasts at the fifth day (120 hours elapsed) isgenerated by the control device based on the generation process of thecomputation model. In the example, two kinds of prediction models (afirst prediction model, a second prediction model) are developed by thecontrol device by using only 42 pieces of samples which arediscriminated to be “differentiated” at the process of the first stagefrom among the 72 pieces of samples.

The first prediction model is a prediction model using five indexes ofthe “combination of the frequency distributions of the ‘breadth’ ateight hours elapsed and 48 hours elapsed”, the “combination of thefrequency distributions of the ‘breadth’ at eight hours elapsed and 32hours elapsed”, the “combination of the frequency distributions of the‘inner radius’ at eight hours elapsed and 24 hours elapsed”, the“combination of the frequency distributions of the ‘length’ at eighthours elapsed and 104 hours elapsed”, the “combination of the frequencydistributions of the ‘hole area’ at eight hours elapsed and 96 hourselapsed”.

FIG. 17 is a graphic chart illustrating a prediction result of eachsample according to the first prediction model in the example. Avertical axis in FIG. 17 represents the degree of differentiation of thesample at the fifth day predicted by the first prediction model. Ahorizontal axis in FIG. 17 represents a value in which the degree ofdifferentiation of the sample is evaluated by a person of skill (anapparent degree of differentiation) at the time of the fifth day. InFIG. 17, one point is plotted on the graph by each sample. Note that itcan be judged that an accuracy of prediction is higher as the point isplotted near a line extending from an upper right to a lower left of thegraph in FIG. 17. In the first prediction model, the prediction accuracy(a right answer rate) of the differentiation rate is 90.5% (error±5%).

Besides, the second prediction model is a prediction model using fiveindexes of the “combination of the frequency distributions of the‘breadth’ at eight hours elapsed and 48 hours elapsed”, the “combinationof the frequency distributions of the ‘breadth’ at eight hours elapsedand 32 hours elapsed”, the “combination of the frequency distributionsof the ‘inner radius’ at eight hours elapsed and 24 hours elapsed”, the“combination of the frequency distributions of an ‘orientation (cellorientation)’ at eight hours elapsed and 16 hours elapsed”, the“combination of the frequency distributions of a ‘modified orientation(variation degree of cell orientation)’ at eight hours elapsed and 24hours elapsed”.

Note that the “orientation” is a feature value representing an anglemade by a major axis of each cell and a horizontal direction (X axis) ofan image. When the values of the “orientation” are the same, the cellsare oriented in the same direction. Besides, the “modified orientation”is a feature value digitizing the angle of each cell under a state inwhich the cells in the image are deformed by a filtering, andcalculating a variation thereof. A value of the “modified orientation”has a characteristic representing a larger value as the angles of thecells are more diverse.

FIG. 18 is a graphic chart representing a prediction result of eachsample according to the second prediction model in the example. A wayhow to look at FIG. 18 is similar to FIG. 17, and therefore, theredundant explanation is not given. In the second prediction model, theprediction of differentiation is performed based on observation resultsup to the second day (48 hours elapsed), and a prediction accuracythereof becomes 85.7% (error ±5%). According to the second predictionmodel in the example, it is possible to perform a qualitative predictionof the differentiation predication of the myoblasts which is normallyvery difficult with high accuracy by the observation results up to thesecond day.

Supplementary Items of Embodiments

(1) In the one embodiment, the example in which the incubated stateevaluating device is incorporated in the control device 41 of theincubator 11 is described, but the incubated state evaluating device ofthe present invention may be made up of an external independent computeracquiring the features of cell morphologies obtained from microscopicimages from the incubator 11 and performing an analysis thereof (thiscase is not illustrated).

(2) In the one embodiment, the example in which the respective functionsof the feature value calculating unit, the frequency distributioncalculating unit, the evaluation information generating unit are enabledby a program by way of software is described., but it goes withoutsaying that these processes may be enabled by an ASIC by way ofhardware.

(3) In the one embodiment, the example in which the control device 41finds the computation model of the evaluation information and theindexes thereof by the FNN is described. However, the incubated stateevaluating device of the present invention may be the one finding thecomputation model of the evaluation information and the indexes thereofby, for example, the other multivariate analysis such as a multipleregression analysis.

Further, the incubated state evaluating device of the present inventionmay combine multi-variate or multi-parameter to generate finalevaluation information by a majority decision (or a weighting average)of calculation results by these computation models. In this case, it ispossible to cover a state of which accuracy is low according to onecomputation model by the other model, and to enhance the accuracy of theevaluation information in a case such that, for example, the MRA iseffective for data of which mixture ratio is low and the FNN iseffective for the data of which mixture ratio is high.

(4) Besides, the incubated state evaluating device may at firstcalculate calculation results by combining plural indexes, to adjust theindexes by a stepwise method when the computation model of theevaluation information is found. Besides, all of the indexes may be usedif the accuracy of data is high.

(5) In the one embodiment, the example in which the variation of thefrequency distribution is found by using an absolute value sum of thedifference between two frequency distributions is described, but thevariation of the frequency distribution may be found from a square sumof the difference between the two frequency distributions. Besides, thecalculation expressions illustrated in the one embodiment are justexamples, and it may be, for example, an n-th equation and so on morethan secondary.

(6) The feature values illustrated in the embodiment and the example areonly examples, and it goes without saying that parameters of the otherfeature values may be used in accordance with the kinds of cells beingthe evaluation object.

The many features and advantages of the embodiments are apparent fromthe detailed specification and, thus, it is intended by the appendedclaims to cover all such features and advantages of the embodiments thatfall within the true spirit and scope thereof. Further, since numerousmodifications and changes will readily occur to those skilled in theart, it is not desired to limit the inventive embodiments to the exactconstruction and operation illustrated and described, and accordinglyall suitable modifications and equivalents may be resorted to, fallingwithin the scope thereof.

1. An incubated state evaluating device, comprising: an image readingunit reading a plurality of images in which a plurality of cellsincubated in an incubation container are image-captured in time series;a feature value calculating unit obtaining, for each of the cellsincluded in the images, each of feature values representingmorphological features of the cells from the images; a frequencydistribution calculating unit obtaining each of frequency distributionsof the feature values corresponding to the respective images.
 2. Theincubated state evaluating device according to claim 1, wherein anevaluation information generating unit generating evaluation informationevaluating an incubated state of the cells in the incubation containerbased on a variation of the frequency distributions between the images.